Volume 17 - Nondestructive Evaluation and Quality Control Part 2 pps

Fig. 20 Robot gripper arm used to maneuver and orient an aluminum aircraft section with the aid of a video camera that helps it see the workpiece and then identifies it from a data base. Courtesy of Lockheed- California Company Additional Applications As machine vision technology evolves and becomes more widely used within industry, additional applications are beginning to emerge. Many of these applications represent special-purpose equipment that has been designed to satisfy a particular need, while others combine two or more of the previously mentioned functions (visual inspection, part identification, and guidance and control). For example, the system shown in Fig. 21 can inspect as well as sort various fasteners and similar multiple-diameter parts. Still other applications reflect machine vision systems that are incorporated into other equipment. Fig. 21 Noncontact digital computer-vision- based fastener inspection system used to gage and then sort parts. Unit measures 11 parameters for up to 24 different fas teners that are stored in a memory and is capable of sorting up to 180 parts per minute or over 10,000 parts per hour. Courtesy of Diffracto Limited Special-Purpose Systems. In the future, special-purpose systems are likely to represent a large, if not the largest, use of machine vision technology. One of the best examples of this type of system is equipment for inspecting PCBs. A number of companies have either developed or are in the process of developing such systems, which are expected to be widely used by the end of the decade. Similar special-purpose equipment is entering the market for the inspection of thick-film substrates and circuits, surface-mounted devices, and photolithographic artwork. Embedded Technology. In the embedded technology are, one of the major uses of machine vision is mask alignment for the production of microelectronic devices. Similarly, vision technology is also becoming widely used for controlling other microelectronic fabrication equipment, such as the automation of wire-bonding machines for connecting integrated circuits to their case. In the future, these two thrusts special-purpose systems and embedded vision technology should result in numerous applications unheard of today. Reference cited in this section 1. "Machine Vision Systems: A Summary and forecast," 2nd ed., Tech Tran Consultants, Inc., 1985 Machine Vision and Robotic Inspection Systems John D. Meyer, Tech Tran Consultants, Inc. Future Outlook The potential for using machine vision in manufacturing applications is enormous. Many inspection operations that are now performed manually could be automated by machine vision, resulting in both reduced costs and improved product quality. However, before machine vision can reach its full potential, several basic improvements in the technology must be made. Limitations of Current Systems At the present time, there are six key issues that must be addressed by vision system developers. Many organizations are attempting to resolve these issues through such developments as improved computer hardware or improved software algorithms, but much work remains to be done to develop effective vision systems that are available at a reasonable cost. The following issues represent basic limitations of commercial vision systems: • Limited 3-D interpretation • Limited interpretation of surfaces • Need for structured environment • Long processing time • High cost • Excessive applications engineering Limited 3-D Interpretation. Most commercial vision systems are two dimensional; that is, they make conclusions about objects from data that are essentially two-dimensional in nature. In many manufacturing situations, an outline of the shape of an object is sufficient to identify it or to determine whether an inspection standard has been achieved. However, in many other operations, such as the inspection of castings, this information is not sufficient. Many more sophisticated operations could be performed with vision systems if the three-dimensional shape of an object could be inferred from an image or a series of images. To accomplish this, vision system suppliers will need to incorporate more sophisticated data interpretation algorithms along with improved system performance (resolution, speed, and discrimination). Limited Interpretation of Surfaces. Complex surface configurations on objects, such as textures, shadows, and overlapping parts, are difficult for vision systems to interpret. Improved gray-scale image formation capabilities have helped somewhat, but vision systems are extremely limited in their ability to analyze the large amounts of data provided by gray-scale image formation. The ability to accurately interpret light intensity variations over the surface of an object, which is so fundamental to human vision, must be refined if vision systems are to be used for such applications as object recognition or inspection from surface characteristics. Need for Structured Environment. Although vision systems, being a form of flexible automation, should be able to eliminate the need for elaborate jigs and fixtures, they still require a relatively orderly environment in most current applications. Vision systems have difficulty dealing with overlapping or touching parts; therefore, workpieces must be presented one at a time to the system. Ideally, a vision system should be able to examine parts as humans do by studying key features no matter how the part is oriented and even if some portions of the parts are obstructed by other overlapping parts. Long Processing Time. There are constraints on the speed of the manufacturing operation in which a vision system can be used. Only a limited number of real-time (30 images per second) systems have begun to appear on the market. However, most real-time systems are used for simple applications rather than more complex tasks. There is generally a trade-off between the processing time required and the degree of complexity of a processing cycle. An ideal vision system would be capable of performing complex three-dimensional analyses of objects, including surface features, in real time. High Cost. Although payback periods for vision systems are generally good (1 year or less for some applications), the basic purchase price of many systems is still prohibitively high to promote widespread use of this technology within the manufacturing industry. Extensive Applications Engineering. It is still nearly impossible to purchase an off-the-shelf vision system and apply it without considerable assistance from a vendor, consultant, or in-house engineering staff. This is partly due to the complexity of real-world applications. Other factors include the limitations of current equipment and the lack of trained personnel within user organizations. Application engineering cost and risk and a shortage of trained technical personnel are major barriers to widespread use of industrial vision systems. Future Developments Many developmental programs are underway, both in private industry as well as in universities and other research organizations, to develop advanced vision systems that are not subject to the limitations discussed previously. The solution to these problems is likely to emerge from several important developments expected to occur during the next decade; these developments are discussed in the following sections. However even if no further improvements are made in machine vision systems, the number of systems in use would continue to grow rapidly. Machine vision systems are beginning to be introduced into applications for which they previously would have not even been considered, because of the complexity of the manufacturing process. Improved Camera Resolution. As solid-state cameras with arrays of 512 × 512 or even 1024 × 1024 pixels are used, image resolution will improve. As a result, the ability of vision systems to sense small features on the surfaces of objects should also improve. Ability to Sense Color. A few developmental vision systems are already available that sense color. The addition of this capability to commercial vision systems would allow the measurement of one more feature in identifying objects. It would also provide a greater degree of discrimination in analyzing surfaces. Effective Range Sensing. This is a prerequisite for three-dimensional interpretation and for certain types of robot vision. Based on research such as that being performed on binocular vision, it is likely that a range-sensing capability will become a standard feature of commercial vision systems within a few years. Ability to Detect Overlap. This capability will approve the ability of vision systems to interpret surfaces and three- dimensional objects. It will also provide a greater degree of flexibility for vision systems. There will no longer be a need to ensure that moving parts on a conveyor are not touching or overlapping, and this will reduce the amount of structure required. Improved Gray-Scale Algorithms. As vision system hardware becomes capable of forming more complex images, the software algorithms for interpreting these images will improve, including the ability to infer shape from changes in light intensity over an image. Robot Wrist-Mounted Vision System. Based on work being performed at a number of organizations, it is likely that an effective wrist-mounted vision system will be available within the next few years. Mounting the camera on the robot's wrist provides the advantage of greatly reducing the degree of structure required during such operations as robot- controlled welding, assembly, or processing. Motion-Sensing Capability. There are two elements being developed in this area. First is the ability of a vision system to create and analyze an image of a moving object. This requires the ability to freeze each frame without blurring for analysis by the computer. Second is the more complex problem of determining the direction of motion of an object and even the magnitude of the velocity. This capability will be valuable in such applications as collision avoidance or tracking moving parts. Parallel Processing of Whole Image. One of the most promising methods of approaching a real-time processing capability is the use of a parallel processing architecture. Several systems currently on the market offer this type of architecture. This approach is likely to be used more extensively in the future. Standardized Software Algorithms. Although some standard vision system application algorithms are available, most programs for current manufacturing applications are custom designed. It is likely that standard programs will become increasingly available for standard application. In addition, programming languages will continue to become more user oriented. Computers Developed Specifically for Vision Systems. Most vision systems today use standard off-the-shelf computers, which tends to limit the data analysis capabilities of the vision system, In the future, especially as sales volumes increase, it is likely that computers will be designed specifically for dedicated use with a vision system. This will reduce processing times and help to reduce system prices. Several systems have been developed with this type of custom computer architecture. Hard-Wired Vision Systems. To overcome the problem of processing speed, some researchers have suggested the use of hard-wired circuitry rather than microprocessor-based systems. This would significantly speed up image processing times, but may result in less system flexibility and limited capabilities. Special-Purpose Systems. As discussed previously, there is a growing trend toward special-purpose, rather than general-purpose, vision systems. This permits the system developer to take advantage of prior knowledge concerning the application and to provide only the features and capabilities required, resulting in more cost-effective systems. A number of vendors have already begun to offer special-purpose systems for such applications as weld seam tracking, robot vision, and PCB inspection. Integration with Other Systems. One of the major problems with the current vision systems is the difficulty in interfacing them with other types of equipment and systems. A number of companies and research organizations are attacking this problem, particularly with respect to special-purpose vision systems. Optical Computing. It is possible to perform image processing using purely optical techniques, as opposed to the traditional approach of converting an image into an electrical signal and analyzing this symbolic representation of the image. In the optical domain, processing steps such as the computation of Fourier transforms take place almost instantaneously. Although optical computing techniques offer considerable promise, it will take a number of years before they become a practical reality. Custom Microelectronic Devices. As the sales volume for vision systems continues to grow, it will become increasingly feasible to implement portions of the system design in custom microelectronic circuits. This will be particularly true for low-level image-processing functions, such as histogram calculations, convolutions, and edge detectors. Such chips should be available within the next few years. Innovative Sensor Configurations. A number of researchers are working on unique vision sensors to improve overall performance. This includes novel sensor configurations, such as annular arrangements of detector elements, as well as other camera concepts, such as multiple spectral detectors that sense energy in more than one portion of the electromagnetic spectrum. Visual Servoing. Several researchers are studying the use of vision systems as an integral feedback component in a motion control system, such as a robot vision system for positioning the manipulator arm. Although vision systems are currently used for robot guidance and control, this is usually accomplished outside the control loop. In visual servoing, on the other hand, the vision system would serve as a position-sensing device or error measurement component on a real- time basis. Machine Vision and Robotic Inspection Systems John D. Meyer, Tech Tran Consultants, Inc. References 1. "Machine Vision Systems: A Summary and forecast," 2nd ed., Tech Tran Consultants, Inc., 1985 2. P. Dunbar, Machine Vision, Byte, Jan 1986 Machine Vision and Robotic Inspection Systems John D. Meyer, Tech Tran Consultants, Inc. Selected References • I. Aleksander, Artificial Vision for Robots, Chapman and Hall, 1983 • D. Ballard and C. Brown, Computer Visions, Prentice-Hall, 1982 • J. Brady, Computer Vision, North-Holland 1982 • O. Faugeras, Fundamentals of Computer Vision, Cambridge University Press, 1983 • J. Hollingum, Machine Vision: Eyes of Automation, Springer-Verlag, 1984 • A. Pugh, Robot Vision, Springer-Verlag, 1983 Guide to Nondestructive Evaluation Techniques John D. Wood, Lehigh University Introduction NONDESTRUCTIVE EVALUATION (NDE) comprises many terms used to describe various activities within the field. Some of these terms are nondestructive testing (NDT), nondestructive inspection (NDI), and nondestructive examination (which has been called NDE, but should probably be called NDEx). These activities include testing, inspection, and examination, which are similar in that they primarily involve looking at (or through) or measuring something about an object to determine some characteristic of the object or to determine whether the object contains irregularities, discontinuities, or flaws. The terms irregularity, discontinuity, and flaw can be used interchangeably to mean something that is questionable in the part or assembly, but specifications, codes, and local usage can result in different definitions for these terms. Because these terms all describe what is being sought through testing, inspection, or examination, the term NDE (nondestructive evaluation) has come to include all the activities of NDT, NDI, and NDEx used to find, locate, size, or determine something about the object or flaws and allow the investigator to decide whether or not the object or flaws are acceptable. A flaw that has been evaluated as rejectable is usually termed a defect. Guide to Nondestructive Evaluation Techniques John D. Wood, Lehigh University Selection of NDE Methods The selection of a useful NDE method or a combination of NDE methods first necessitates a clear understanding of the problem to be solved. It is then necessary to single out from the various possibilities those NDE methods that are suitable for further consideration; this is done by reviewing the articles in this Volume and in the technical literature. Several different ways of comparing the selected NDE methods are presented in this article, but there is no completely acceptable system of comparison, because the results are highly dependent on the application. Therefore, it is recommended that a comparison be developed specifically for each NDE area and application. The final validation of any NDE protocol will depend on acceptance tests conducted using appropriate calibration standards. Nondestructive evaluation can be conveniently divided into nine distinct areas: • Flaw detection and evaluation • Leak detection and evaluation • Metrology (measurement of dimension) and evaluation • Location determination and evaluation • Structure or microstructure characterization • Estimation of mechanical and physical properties • Stress (strain) and dynamic response determination • Signature analysis • Chemical composition determination Because two of these areas signature analysis and chemical composition determination are usually not considered when NDE applications are discussed and are therefore not covered in this Volume, they will not be discussed further. Information on these subjects can, however, be found in Materials Characterization, Volume 10 of ASM Handbook, formerly 9th Edition Metals Handbook. The remaining seven areas are vastly different and therefore will be covered separately, along with a discussion of the selection of specific NDE methods * for each. Note cited in this section * Throughout this article the term method is used to describe the various nondestructive testing disciplines (for example, ultrasonic testing) within which variou s test techniques may exist (for example, immersion or contact ultrasonic testing). Guide to Nondestructive Evaluation Techniques John D. Wood, Lehigh University Flaw Detection and Evaluation Flaw detection is usually considered the most important aspect of NDE. There are many conceivable approaches to selecting NDE methods. One approach is to consider that there are only six primary factors involved in selecting an NDE method(s): • The reason(s) for performing the NDE • The type(s) of flaws of interest in the object • The size and orientation of flaw that is rejectable • The anticipated location of the flaws of interest in the object • The size and shape of the object • The characteristics of the material to be evaluated The most important question to be answered before an NDE method can be selected is, What is the reason(s) for choosing an NDE procedure? There are a number of possible reasons, such as: • Determining whether an object is acceptable after each fabrication step; this can be called in- process NDE or in-process inspection • Determining whether an object is acceptable for final use; this can be called final NDE or final inspection • Determining whether an existing object already in use is acceptable for continued use; this can be called in-service NDE or in-service inspection After the reasons for selecting NDE have been established, one must specify which types of flaws are rejectable, the size and orientation of flaws that are rejectable, and the locations of flaws that can cause the object to become rejectable. The type, size, orientation, and location of flaws that will cause a rejection must be determined if possible, using stress analysis and/or fracture mechanics calculations. If definitive calculations are not economically feasible, the type, size, and orientation of flaw that will cause the object to be rejected must be estimated with an appropriate safety factor. The type, size, orientation, and location of the rejectable flaw are often dictated by a code, standard, or requirement, such as the American Society of Mechanical Engineers Pressure Vessel Code, a Nuclear Regulatory Commission requirement, or the American Welding Society Structural Welding Code. If one of these applies to the object under consideration, the information needed will be available in the appropriate document. Volumetric and Planar Flaws. Once the size and orientation of the rejectable flaw have been established, it is necessary to determine which types of flaws are rejectable. In general, there are two types of flaws: volumetric and planar. Volumetric flaws can be described by three dimensions or a volume. Table 1 lists some of the various types of volumetric flaws, along with useful NDE detection methods. Planar flaws are thin in one dimension but larger in the other two dimensions. Table 2 lists some of the various types of planar flaws, along with appropriate NDE detection methods. Table 1 Volumetric flaw classification and NDE detection methods Volumetric flaws Porosity Inclusions Slag Tungsten Other Shrinkage Holes and voids Corrosion thinning Corrosion pitting NDE detection methods Visual (surface) Replica (surface) Liquid penetrant (surface) Magnetic particle (surface and subsurface) Eddy current Microwave Ultrasonic Radiography X-ray computed tomography Neutron radiography Thermography Optical holography Speckle metrology Digital image enhancement (surface) Table 2 Planar flaw classification and NDE detection methods Planar flaws Seams Lamination Lack of bonding Forging or rolling lap Casting cold shut Heat treatment cracks Grinding cracks Plating cracks Fatigue cracks Stress-corrosion cracks Welding cracks Lack of fusion Incomplete penetration Brazing debond NDE detection methods Visual Replication microscopy Magnetic particle Magnetic field Eddy current Microwave Electric current perturbation Magabsorption Ultrasonic Acoustic emission Thermography Flaw Location, Shape, and Size. In addition to classifying flaws as volumetric or planar, it is necessary to consider the locations of the flaws in the object. Flaws can be conveniently classified as surface flaws or as interior flaws that do not intercept the surface. Table 3 lists NDE methods used to detect surface and interior flaws. Table 3 NDE methods for the detection of surface and interior flaws Surface Visual Replica Liquid penetrant Magnetic particle Magnetic field Electric current Magabsorption Eddy current Ultrasonic Acoustic emission Thermography Optical holography Speckle metrology Acoustic holography Digital image enhancement Acoustic microscopy Interior Magnetic particle (limited use) Magnetic field Electric current perturbation Magabsorption Eddy current Microwave Ultrasonic Acoustic emission Radiography X-ray computed tomography Neutron radiography Thermography (possible) Optical holography (possible) Acoustic holography (possible) Two additional factors that affect NDE method selection are the shape and size of the object to be evaluated. Tables 4 and 5 compare NDE techniques for varying size (thickness) and shape. Table 4 Comparison of NDE methods based on size of object to be evaluated The thickness or dimension limitation is only approximate because the exact value depends on the specific physical properties of the material being evaluated. [...]... 1 × 1 0 -2 1 × 1 0-3 7.6 × 1 0-3 5 × 1 0-3 5 × 1 0-4 3.8 × 1 0-3 1 × 1 0-3 1 × 1 0-4 7.6 × 1 0-4 5 × 1 0-4 5 × 1 0-5 3.8 × 1 0-4 1 × 1 0-4 1 × 1 0-5 7.6 × 1 0-5 5 × 1 0-5 5 × 1 0-6 3.8 × 1 0-5 1 × 1 0-5 1 × 1 0-6 7.6 × 1 0-6 5 × 1 0-6 5 × 1 0-7 3.8 × 1 0-6 1 × 1 0-6 1 × 1 0-7 7.6 × 1 0-7 5 × 1 0-7 5 × 1 0-8 3.8 × 1 0-7 1 × 1 0-7 1 × 1 0-8 7.6 × 1 0-8 1 × 1 0-8 1 × 1 0-9 7.6 × 1 0-9 1 × 1 0-9 1 × 1 0-1 0 7.6 × 1 0-1 0 1 × 1 0-1 0 1 × 1 0-1 1 7.6... 450 Hydrogen 12. 5 4.10 12. 5 41.0 12. 5 0.4 92 12. 5 500 125 125 0 Water 4 .2 1.4 4 .2 14 4 .2 0.16 4 .2 170 42 420 Helium 19.6 6.43 19.6 64.3 19.6 0.7 72 19.6 784 196 1960 Nitrogen 6.7 2. 2 6.7 22 6.7 0 .26 6.7 26 8 67 670 Neon 14.0 4.59 14.0 45.9 14.0 0.55 14.0 560 140 1400 Oxygen 7 .2 2.4 7 .2 24 7 .2 0 .28 7 .2 290 72 720 Source: Ref 1 (a) Approximately atmospheric pressure (b) 1 nm (nanometer) = 1 0-9 m The level... spectrometer 1 0-3 to 1 0-5 1 0-3 to 1 0-1 0 Electron capture 1 0-6 to 1 0-1 1 Colorimetric developer 1 to 1 0-8 Bubble test liquid film 1 0-1 to 1 0-5 1 0-1 to 1 0-5 Bubble test immersion 1 to 1 0-6 Hydrostatic test 1 to 1 0 -2 Pressure increase 1 to 1 0-4 1 to 1 0-4 Pressure decrease/flow 1 to 1 0-3 Liquid tracer 1 to 1 0-4 1 to 1 0-4 High voltage 1 to 1 0-4 Halogen (heated anode) 1 0-1 to 1 0-6 1 0-1 to 1 0-5 Thermal conductivity... Table 2 Mean free path lengths of various atmospheric gases at 20 °C (68 °F) and various vacuum pressures Gas Mean free path length at indicated absolute pressure 1 μPa (7.5 × 1 0-9 torr) 1 mPa (7.5 × 1 0-6 torr) 1 Pa (7.5 × 1 0-3 torr) 1 kPa (7.5 torr) 100 kPa (750 torr)(a) km ft × 104 m ft mm in μ μin nm(b) Å Air 6.8 2. 2 6.8 22 6.8 0 .27 6.8 27 2 68 680 Argon 7 .2 2.4 7 .2 24 7 .2 0 .28 7 .2 290 72 720 Carbon... is volume, and K is a constant) enables one to convert these units into more meaningful volumetric terms; these conversions are listed in Table 1, along with conversions for pressures and volumes Table 1 Conversion factors for quantities related to leak testing std cm3/s Pa · m3/s, at 0 °C ( 32 °F) torr · L/s, at 0 °C ( 32 °F) 1 0.1 7.6 × 1 0-1 1 × 1 0-1 1 × 1 0 -2 7.6 × 1 0 -2 5 × 1 0 -2 5 × 1 0-3 3.8 × 1 0 -2 ... 1 0-7 1 × 1 0-7 1 × 1 0-8 7.6 × 1 0-8 1 × 1 0-8 1 × 1 0-9 7.6 × 1 0-9 1 × 1 0-9 1 × 1 0-1 0 7.6 × 1 0-1 0 1 × 1 0-1 0 1 × 1 0-1 1 7.6 × 1 0-1 1 1 × 1 0-1 1 1 × 1 0-1 2 7.6 × 1 0-1 2 1 × 1 0-1 2 1 × 1 0-1 3 7.6 × 1 0-1 3 1 × 1 0-1 3 1 × 1 0-1 4 7.6 × 1 0-1 4 Types of Leaks There are two basic types of leaks: real leaks and virtual leaks A real leak is an essentially localized leak, that is, a discrete passage through which fluid may flow... to 1 0-6 1 0-1 to 1 0-5 Thermal conductivity (He) 1 to 1 0-5 Gage 1 0-1 to 1 0-7 Radioactive tracer 1 0-1 3 Infrared 1 to 1 0-5 Acoustic 1 to 1 0 -2 1 to 1 0 -2 Smoke tracer 1 to 1 0 -2 Source: Ref 2 Leak detection by monitoring changes in the pressure of the internal fluid is often used when leak detection equipment is not immediately available For the most part, detection can be accomplished with instruments... p 44 12 F Masuyama, K Setoguchi, H Haneda, and F Nanjo, Findings on Creep-Fatigue Damage in Pressure Parts of Long-Term Service-Exposed Thermal Power Plants, in Residual Life Assessment Nondestructive Examination and Nuclear Heat Exchanger Materials, PVP-Vol 9 8-1 , Proceedings of the Pressure Vessels and Piping Conference, American Society of Mechanical Engineers, 1985, p 79 13 T Fushimi, "Life Evaluation. .. this article is standard atmosphere cubic centimeter per second, where standard conditions are defined as 1 atm = 101. 325 kPa and standard temperature is 27 3.15 K (0 °C) The conversion factor is std cm3/s = 4.46 × 1 0-5 mol/s Another unit of leak rate generally accepted is the gas-flow rate that causes a pressure rise per unit of time of 1 m Hg/s (40 in Hg/s) in a volume of 1 L (0 .26 gal.) This is termed... Afrouz, M.J Collins, and R Pilkington, Microstructural Examination of 1Cr-0.5Mo Steel During Creep, Met Technol., Vol 10, 1983, p 461 Replication Microscopy Techniques for NDE A.R Marder, Energy Research Center, Lehigh University References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 "Standard Practice for Production and Evaluation of Field Metallographic Replicas," E 51 2- 8 7, Annual Book of ASTM Standards, American . • I. Aleksander, Artificial Vision for Robots, Chapman and Hall, 1983 • D. Ballard and C. Brown, Computer Visions, Prentice-Hall, 19 82 • J. Brady, Computer Vision, North-Holland 19 82 • O. Faugeras,. distinct areas: • Flaw detection and evaluation • Leak detection and evaluation • Metrology (measurement of dimension) and evaluation • Location determination and evaluation • Structure or microstructure. are x-ray radiography, x-ray computed tomography, and neutron radiography. These techniques and their selection are discussed in separate articles in this Volume. Guide to Nondestructive Evaluation